| Version: | 1.0.1 |
| Title: | Tools for qPCR |
| Description: | PKG_DESC. |
| URL: | https://github.com/lixiang117423/qPCRtools |
| BugReports: | https://github.com/lixiang117423/qPCRtools/issues |
| License: | MIT + file LICENSE |
| Imports: | broom, dplyr, ggplot2, ggpmisc, ggthemes, kableExtra, magrittr, multcomp, rstatix, stats, tibble, tidyr |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-11-02 09:16:53 UTC; lixia |
| Author: | Xiang LI [cre, aut] |
| Maintainer: | Xiang LI <lixiang117423@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-11-02 13:10:05 UTC |
Standard Curve Calculation.
Description
The function can calculate the standard curve. At the same time, it can get the amplification efficiency of primer(s). Based on the amplification efficiency, we can know which method can be used to calculate the expression level.
Arguments
cq.table |
The data frame of the position and Cq value. |
concen.table |
The data frame of the position and concentration. |
highest.concen |
The highest concentration for calculation. |
lowest.concen |
The lowest concentration for calculation. |
dilution |
Dilution factor of cDNA template. The default value is 4. |
by.mean |
Calculation by mean Cq value or not. The default value is TRUE. |
Value
A list.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df.1.path <- system.file("examples", "calsc.cq.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "calsc.info.txt", package = "qPCRtools")
df.1 <- read.table(df.1.path, header = TRUE)
df.2 <- read.table(df.2.path, header = TRUE)
CalCurve(
cq.table = df.1,
concen.table = df.2,
lowest.concen = 4,
highest.concen = 4096,
dilu = 4,
by = "mean"
) -> p
p[["table"]]
p[["figure"]]
Calculate expression using standard curve.
Description
Calculate expression using standard curve.
Arguments
cq.table |
The data frame of the position and cq value. |
design.table |
The data frame of the position and corresponding information. |
ref.gene |
The name of reference gene. |
Value
A list contain a table and a figure.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df1.path <- system.file("examples", "dct.cq.txt", package = "qPCRtools")
df2.path <- system.file("examples", "dct.design.txt", package = "qPCRtools")
cq.table <- read.table(df1.path, sep = ",", header = TRUE)
design.table <- read.table(df2.path, sep = ",", header = TRUE)
CalExp2dCt(cq.table,
design.table,
ref.gene = "Actin"
) -> res
Calculate expression using standard curve.
Description
Calculate expression using standard curve.
Arguments
cq.table |
The data frame of the position and cq value. |
design.table |
The data frame of the position and corresponding information. |
correction |
Correct expression value by reference gene. |
ref.gene |
The name of reference gene. |
ref.group |
The name of reference group. |
stat.method |
Statistical method. |
remove.outliers |
Remove the outliers of each group and gene, or not. |
fig.type |
Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'. |
fig.ncol |
Number of columes of figure. |
Value
A list contain a table and a figure.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df1.path = system.file("examples", "ddct.cq.txt", package = "qPCRtools")
df2.path = system.file("examples", "ddct.design.txt", package = "qPCRtools")
cq.table = read.table(df1.path, header = TRUE)
design.table = read.table(df2.path, header = TRUE)
CalExp2ddCt(cq.table,
design.table,
ref.gene = "OsUBQ",
ref.group = "CK",
stat.method = "t.test",
remove.outliers = TRUE,
fig.type = "box",
fig.ncol = NULL) -> res
res[["table"]]
res[["figure"]]
Calculate expression using standard curve.
Description
Calculate expression using standard curve.
Arguments
cq.table |
The data frame of the position and Cq value. |
design.table |
The data frame of the position and corresponding information. |
correction |
Correct expression value by reference gene. |
ref.gene |
The name of reference gene. |
stat.method |
Statistical method. |
ref.group |
The name of reference group. |
fig.type |
Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'. |
fig.ncol |
Number of columes of figure. |
Value
A list contain a table and a figure.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df1.path = system.file("examples", "cal.exp.curve.cq.txt", package = "qPCRtools")
df2.path = system.file("examples", "cal.expre.curve.sdc.txt", package = "qPCRtools")
df3.path = system.file("examples", "cal.exp.curve.design.txt", package = "qPCRtools")
cq.table = read.table(df1.path, header = TRUE)
curve.table = read.table(df2.path, sep = "\t", header = TRUE)
design.table = read.table(df3.path, header = TRUE)
CalExpCurve(
cq.table,
curve.table,
design.table,
correction = TRUE,
ref.gene = "OsUBQ",
stat.method = "t.test",
ref.group = "CK",
fig.type = "box",
fig.ncol = NULL) -> res
res[["table"]]
res[["figure"]]
Calculate expression using standard curve.
Description
Calculate expression using standard curve.
Arguments
cq.table |
The data frame of the position and cq value. |
design.table |
The data frame of the position and corresponding information. |
correction |
Correct expression value by reference gene. |
ref.gene |
The name of reference gene. |
ref.group |
The name of reference group. |
stat.method |
Statistical method. |
fig.type |
Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'. |
fig.ncol |
Number of columes of figure. |
Value
A list contain a table and a figure.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df1.path <- system.file("examples", "cal.expre.rqpcr.cq.txt", package = "qPCRtools")
df2.path <- system.file("examples", "cal.expre.rqpcr.design.txt", package = "qPCRtools")
cq.table <- read.table(df1.path, header = TRUE)
design.table <- read.table(df2.path, header = TRUE)
CalExpRqPCR(cq.table,
design.table,
ref.gene = NULL,
ref.group = "CK",
stat.method = "t.test",
fig.type = "box",
fig.ncol = NULL
) -> res
res[["table"]]
res[["figure"]]
Calculate RNA volume for reverse transcription.
Description
The first step of qPCR is usually the preparation of cDNA. We need to calculate the column of RNA for reverse transcription to cDNA. So, if we have the concentration of RNA, we can use the function 'CalRTable' to do that.
Arguments
data |
A data.frame contained the sample names and the concentration value. The default unit of concentration is ng/uL. |
template |
A data.frame contained the information of reverse transcription. In this data.frame there must be a column called 'all'. |
RNA.weight |
RNA weight required for reverse transcription. Default is 1 ug. |
Value
A list contain a table and a figure.
Author(s)
Xiang LI <lixiang117423@gmail.com>
Examples
df.1.path <- system.file("examples", "crtv.data.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "crtv.template.txt", package = "qPCRtools")
df.1 <- read.table(df.1.path, sep = "\t", header = TRUE)
df.2 <- read.table(df.2.path, sep = "\t", header = TRUE)
result <- CalRTable(data = df.1, template = df.2, RNA.weight = 2)
head(result)